Low Delay Short Word Length Sigma Delta Active Noise Control
Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This de...
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Veröffentlicht in: | IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2021-09, Vol.68 (9), p.3746-3757 |
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description | Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This delay can be reduced by increasing the sampling frequency at the expense of a significant increase in computational complexity. Sigma-delta AD and DA converters work at high sampling frequencies, but their use in ANC is limited due to the delay of AA and RC filters. This work proposes removing the AA and RC filters of the sigma-delta converters and using the oversampled signals directly in an ANC system. This proposal allows the ANC system to be implemented at a large sample frequency but using signals with fewer bits per sample (word-length). This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors' knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution. |
doi_str_mv | 10.1109/TCSI.2021.3096180 |
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This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors' knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution.</description><identifier>ISSN: 1549-8328</identifier><identifier>EISSN: 1558-0806</identifier><identifier>DOI: 10.1109/TCSI.2021.3096180</identifier><identifier>CODEN: ITCSCH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Active noise control ; Active noise control (ANC) ; Complexity ; Converters ; Delay ; Delays ; Feedforward systems ; FxLMS ; Headphones ; Least mean squares ; Least mean squares algorithm ; Noise control ; RC circuits ; Sampling ; sigma delta (ΣΔ) ; Sigma-delta modulation ; Signal processing algorithms ; Transducers ; Transfer functions</subject><ispartof>IEEE transactions on circuits and systems. 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C.</creatorcontrib><creatorcontrib>Gerald, Jose Antonio Beltran</creatorcontrib><title>Low Delay Short Word Length Sigma Delta Active Noise Control</title><title>IEEE transactions on circuits and systems. I, Regular papers</title><addtitle>TCSI</addtitle><description>Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This delay can be reduced by increasing the sampling frequency at the expense of a significant increase in computational complexity. Sigma-delta AD and DA converters work at high sampling frequencies, but their use in ANC is limited due to the delay of AA and RC filters. This work proposes removing the AA and RC filters of the sigma-delta converters and using the oversampled signals directly in an ANC system. This proposal allows the ANC system to be implemented at a large sample frequency but using signals with fewer bits per sample (word-length). This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors' knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution.</description><subject>Active noise control</subject><subject>Active noise control (ANC)</subject><subject>Complexity</subject><subject>Converters</subject><subject>Delay</subject><subject>Delays</subject><subject>Feedforward systems</subject><subject>FxLMS</subject><subject>Headphones</subject><subject>Least mean squares</subject><subject>Least mean squares algorithm</subject><subject>Noise control</subject><subject>RC circuits</subject><subject>Sampling</subject><subject>sigma delta (ΣΔ)</subject><subject>Sigma-delta modulation</subject><subject>Signal processing algorithms</subject><subject>Transducers</subject><subject>Transfer functions</subject><issn>1549-8328</issn><issn>1558-0806</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKs_QLwEPG-dSTa7CXgp61dh0UMrHkN2N9tuaZuapEr_vV1aPM0L87wz8BByizBCBPUwK6aTEQOGIw4qQwlnZIBCyAQkZOd9TlUiOZOX5CqEJQBTwHFAHkv3S5_syuzpdOF8pF_ON7S0m3lc0Gk3X5t-Gw0d17H7sfTddcHSwm2id6trctGaVbA3pzkkny_Ps-ItKT9eJ8W4TGqmeExai7YRkLOqgbxpLW9y2TBWGTCpaCWTknHMa5VileWmSutMqgpA1ExIlK3kQ3J_vLv17ntnQ9RLt_Obw0vNRAbI04zhgcIjVXsXgret3vpubfxeI-heku4l6V6SPkk6dO6Onc5a-8-rVPE8ZfwPc4pgrA</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Lopes, Paulo A. 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C. ; Gerald, Jose Antonio Beltran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c293t-fe1ed5072bd07dfe3d78d22ba0a45f82882317c941b67ab4c689b005c25818f83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Active noise control</topic><topic>Active noise control (ANC)</topic><topic>Complexity</topic><topic>Converters</topic><topic>Delay</topic><topic>Delays</topic><topic>Feedforward systems</topic><topic>FxLMS</topic><topic>Headphones</topic><topic>Least mean squares</topic><topic>Least mean squares algorithm</topic><topic>Noise control</topic><topic>RC circuits</topic><topic>Sampling</topic><topic>sigma delta (ΣΔ)</topic><topic>Sigma-delta modulation</topic><topic>Signal processing algorithms</topic><topic>Transducers</topic><topic>Transfer functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lopes, Paulo A. C.</creatorcontrib><creatorcontrib>Gerald, Jose Antonio Beltran</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on circuits and systems. I, Regular papers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lopes, Paulo A. C.</au><au>Gerald, Jose Antonio Beltran</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Low Delay Short Word Length Sigma Delta Active Noise Control</atitle><jtitle>IEEE transactions on circuits and systems. I, Regular papers</jtitle><stitle>TCSI</stitle><date>2021-09-01</date><risdate>2021</risdate><volume>68</volume><issue>9</issue><spage>3746</spage><epage>3757</epage><pages>3746-3757</pages><issn>1549-8328</issn><eissn>1558-0806</eissn><coden>ITCSCH</coden><abstract>Active noise control (ANC) requires a controller with a small delay. However, in most ANC applications, this delay is significant due to the typically low sample rate, the anti-aliasing (AA), and reconstruction filters (RC) of the analog to digital (AD) and digital to analog (DA) converters. This delay can be reduced by increasing the sampling frequency at the expense of a significant increase in computational complexity. Sigma-delta AD and DA converters work at high sampling frequencies, but their use in ANC is limited due to the delay of AA and RC filters. This work proposes removing the AA and RC filters of the sigma-delta converters and using the oversampled signals directly in an ANC system. This proposal allows the ANC system to be implemented at a large sample frequency but using signals with fewer bits per sample (word-length). This is the first short-word length implementation of the least mean squares (LMS) algorithm to the authors' knowledge. Gains of up to 6 times may be achieved in the computational complexity when compared with a long-word length implementation. Theoretical and simulation results validate the current solution.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSI.2021.3096180</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-5230-2137</orcidid><orcidid>https://orcid.org/0000-0002-9045-0413</orcidid></addata></record> |
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subjects | Active noise control Active noise control (ANC) Complexity Converters Delay Delays Feedforward systems FxLMS Headphones Least mean squares Least mean squares algorithm Noise control RC circuits Sampling sigma delta (ΣΔ) Sigma-delta modulation Signal processing algorithms Transducers Transfer functions |
title | Low Delay Short Word Length Sigma Delta Active Noise Control |
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